| DATAFLOW | LAST UPDATE | freq | meat | meatitem | unit | geo | TIME_PERIOD | OBS_VALUE | OBS_FLAG | |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | ESTAT:APRO_MT_PWGTM(1.0) | 29/04/22 23:00:00 | M | B1200 | SL | THS_T | IE | 1974-01 | 26.0 | NaN |
| 1 | ESTAT:APRO_MT_PWGTM(1.0) | 29/04/22 23:00:00 | M | B1200 | SL | THS_T | IE | 1974-02 | 22.8 | NaN |
| 2 | ESTAT:APRO_MT_PWGTM(1.0) | 29/04/22 23:00:00 | M | B1200 | SL | THS_T | IE | 1974-03 | 20.4 | NaN |
| 3 | ESTAT:APRO_MT_PWGTM(1.0) | 29/04/22 23:00:00 | M | B1200 | SL | THS_T | IE | 1974-04 | 18.5 | NaN |
| 4 | ESTAT:APRO_MT_PWGTM(1.0) | 29/04/22 23:00:00 | M | B1200 | SL | THS_T | IE | 1974-05 | 22.3 | NaN |
| Number of Variables | 10 |
|---|---|
| Number of Rows | 4287 |
| Missing Cells | 4287 |
| Missing Cells (%) | 10.0% |
| Duplicate Rows | 0 |
| Duplicate Rows (%) | 0.0% |
| Total Size in Memory | 2.3 MB |
| Average Row Size in Memory | 560.2 B |
| Variable Types |
|
| OBS_VALUE has 165 (3.85%) missing values | Missing |
|---|---|
| OBS_FLAG has 4122 (96.15%) missing values | Missing |
| OBS_VALUE is skewed | Skewed |
| TIME_PERIOD has a high cardinality: 627 distinct values | High Cardinality |
| DATAFLOW has constant value "ESTAT:APRO_MT_PWGTM(1.0)" | Constant |
| LAST UPDATE has constant value "29/04/22 23:00:00" | Constant |
| freq has constant value "M" | Constant |
| meatitem has constant value "SL" | Constant |
| unit has constant value "THS_T" | Constant |
| OBS_FLAG has constant value "c" | Constant |
| DATAFLOW has constant length 24 | Constant Length |
|---|---|
| LAST UPDATE has constant length 17 | Constant Length |
| freq has constant length 1 | Constant Length |
| meat has constant length 5 | Constant Length |
| meatitem has constant length 2 | Constant Length |
| unit has constant length 5 | Constant Length |
| geo has constant length 2 | Constant Length |
| TIME_PERIOD has constant length 7 | Constant Length |
| OBS_FLAG has constant length 1 | Constant Length |
categorical
| Approximate Distinct Count | 1 |
|---|---|
| Approximate Unique (%) | 0.0% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Memory Size | 372.6 KB |
| Mean | 24 |
|---|---|
| Standard Deviation | 0 |
| Median | 24 |
| Minimum | 24 |
| Maximum | 24 |
| 1st row | ESTAT:APRO_MT_PWGT... |
|---|---|
| 2nd row | ESTAT:APRO_MT_PWGT... |
| 3rd row | ESTAT:APRO_MT_PWGT... |
| 4th row | ESTAT:APRO_MT_PWGT... |
| 5th row | ESTAT:APRO_MT_PWGT... |
| Count | 68592 |
|---|---|
| Lowercase Letter | 0 |
| Space Separator | 0 |
| Uppercase Letter | 68592 |
| Dash Punctuation | 0 |
| Decimal Number | 8574 |
categorical
| Approximate Distinct Count | 1 |
|---|---|
| Approximate Unique (%) | 0.0% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Memory Size | 343.3 KB |
| Mean | 17 |
|---|---|
| Standard Deviation | 0 |
| Median | 17 |
| Minimum | 17 |
| Maximum | 17 |
| 1st row | 29/04/22 23:00:00 |
|---|---|
| 2nd row | 29/04/22 23:00:00 |
| 3rd row | 29/04/22 23:00:00 |
| 4th row | 29/04/22 23:00:00 |
| 5th row | 29/04/22 23:00:00 |
| Count | 0 |
|---|---|
| Lowercase Letter | 0 |
| Space Separator | 4287 |
| Uppercase Letter | 0 |
| Dash Punctuation | 0 |
| Decimal Number | 51444 |
categorical
| Approximate Distinct Count | 1 |
|---|---|
| Approximate Unique (%) | 0.0% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Memory Size | 276.3 KB |
| Mean | 1 |
|---|---|
| Standard Deviation | 0 |
| Median | 1 |
| Minimum | 1 |
| Maximum | 1 |
| 1st row | M |
|---|---|
| 2nd row | M |
| 3rd row | M |
| 4th row | M |
| 5th row | M |
| Count | 4287 |
|---|---|
| Lowercase Letter | 0 |
| Space Separator | 0 |
| Uppercase Letter | 4287 |
| Dash Punctuation | 0 |
| Decimal Number | 0 |
categorical
| Approximate Distinct Count | 5 |
|---|---|
| Approximate Unique (%) | 0.1% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Memory Size | 293.1 KB |
| Mean | 5 |
|---|---|
| Standard Deviation | 0 |
| Median | 5 |
| Minimum | 5 |
| Maximum | 5 |
| 1st row | B1200 |
|---|---|
| 2nd row | B1200 |
| 3rd row | B1200 |
| 4th row | B1200 |
| 5th row | B1200 |
| Count | 4287 |
|---|---|
| Lowercase Letter | 0 |
| Space Separator | 0 |
| Uppercase Letter | 4287 |
| Dash Punctuation | 0 |
| Decimal Number | 17148 |
categorical
| Approximate Distinct Count | 1 |
|---|---|
| Approximate Unique (%) | 0.0% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Memory Size | 280.5 KB |
| Mean | 2 |
|---|---|
| Standard Deviation | 0 |
| Median | 2 |
| Minimum | 2 |
| Maximum | 2 |
| 1st row | SL |
|---|---|
| 2nd row | SL |
| 3rd row | SL |
| 4th row | SL |
| 5th row | SL |
| Count | 8574 |
|---|---|
| Lowercase Letter | 0 |
| Space Separator | 0 |
| Uppercase Letter | 8574 |
| Dash Punctuation | 0 |
| Decimal Number | 0 |
categorical
| Approximate Distinct Count | 1 |
|---|---|
| Approximate Unique (%) | 0.0% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Memory Size | 293.1 KB |
| Mean | 5 |
|---|---|
| Standard Deviation | 0 |
| Median | 5 |
| Minimum | 5 |
| Maximum | 5 |
| 1st row | THS_T |
|---|---|
| 2nd row | THS_T |
| 3rd row | THS_T |
| 4th row | THS_T |
| 5th row | THS_T |
| Count | 17148 |
|---|---|
| Lowercase Letter | 0 |
| Space Separator | 0 |
| Uppercase Letter | 17148 |
| Dash Punctuation | 0 |
| Decimal Number | 0 |
categorical
| Approximate Distinct Count | 2 |
|---|---|
| Approximate Unique (%) | 0.0% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Memory Size | 280.5 KB |
| Mean | 2 |
|---|---|
| Standard Deviation | 0 |
| Median | 2 |
| Minimum | 2 |
| Maximum | 2 |
| 1st row | IE |
|---|---|
| 2nd row | IE |
| 3rd row | IE |
| 4th row | IE |
| 5th row | IE |
| Count | 8574 |
|---|---|
| Lowercase Letter | 0 |
| Space Separator | 0 |
| Uppercase Letter | 8574 |
| Dash Punctuation | 0 |
| Decimal Number | 0 |
categorical
| Approximate Distinct Count | 627 |
|---|---|
| Approximate Unique (%) | 14.6% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Memory Size | 301.4 KB |
| Mean | 7 |
|---|---|
| Standard Deviation | 0 |
| Median | 7 |
| Minimum | 7 |
| Maximum | 7 |
| 1st row | 1974-01 |
|---|---|
| 2nd row | 1974-02 |
| 3rd row | 1974-03 |
| 4th row | 1974-04 |
| 5th row | 1974-05 |
| Count | 0 |
|---|---|
| Lowercase Letter | 0 |
| Space Separator | 0 |
| Uppercase Letter | 0 |
| Dash Punctuation | 4287 |
| Decimal Number | 25722 |
numerical
| Approximate Distinct Count | 2587 |
|---|---|
| Approximate Unique (%) | 62.8% |
| Missing | 165 |
| Missing (%) | 3.8% |
| Infinite | 0 |
| Infinite (%) | 0.0% |
| Memory Size | 64.4 KB |
| Mean | 41.4682 |
| Minimum | 0.03 |
| Maximum | 179.53 |
| Zeros | 0 |
| Zeros (%) | 0.0% |
| Negatives | 0 |
| Negatives (%) | 0.0% |
| Minimum | 0.03 |
|---|---|
| 5-th Percentile | 0.3605 |
| Q1 | 4.905 |
| Median | 24.3 |
| Q3 | 75.2275 |
| 95-th Percentile | 125.0615 |
| Maximum | 179.53 |
| Range | 179.5 |
| IQR | 70.3225 |
| Mean | 41.4682 |
|---|---|
| Standard Deviation | 40.8802 |
| Variance | 1671.1921 |
| Sum | 170932 |
| Skewness | 0.8088 |
| Kurtosis | -0.4308 |
| Coefficient of Variation | 0.9858 |
categorical
| Approximate Distinct Count | 1 |
|---|---|
| Approximate Unique (%) | 0.6% |
| Missing | 4122 |
| Missing (%) | 96.2% |
| Memory Size | 10.6 KB |
| Mean | 1 |
|---|---|
| Standard Deviation | 0 |
| Median | 1 |
| Minimum | 1 |
| Maximum | 1 |
| 1st row | c |
|---|---|
| 2nd row | c |
| 3rd row | c |
| 4th row | c |
| 5th row | c |
| Count | 165 |
|---|---|
| Lowercase Letter | 165 |
| Space Separator | 0 |
| Uppercase Letter | 0 |
| Dash Punctuation | 0 |
| Decimal Number | 0 |
| meat | geo | TIME_PERIOD | OBS_VALUE | OBS_FLAG | |
|---|---|---|---|---|---|
| 0 | B1200 | IE | 1974-01 | 26.0 | <NA> |
| 1 | B1200 | IE | 1974-02 | 22.8 | <NA> |
| 2 | B1200 | IE | 1974-03 | 20.4 | <NA> |
| 3 | B1200 | IE | 1974-04 | 18.5 | <NA> |
| 4 | B1200 | IE | 1974-05 | 22.3 | <NA> |
| TIME_PERIOD | geo | OBS_FLAG | B1200 | B3100 | B4110 | B7100 | B7200 | |
|---|---|---|---|---|---|---|---|---|
| 0 | 1970-01 | IT | NaN | 76.0 | 95.18 | NaN | NaN | NaN |
| 1 | 1970-02 | IT | NaN | 68.2 | 44.60 | NaN | NaN | NaN |
| 2 | 1970-03 | IT | NaN | 78.8 | 29.27 | NaN | NaN | NaN |
| 3 | 1970-04 | IT | NaN | 81.4 | 22.64 | NaN | NaN | NaN |
| 4 | 1970-05 | IT | NaN | 79.9 | 20.32 | NaN | NaN | NaN |
| time_period | geo | obs_flag | adult_cattle | pork | lamb | chicken | duck | |
|---|---|---|---|---|---|---|---|---|
| 0 | 1970-01 | IT | NaN | 76.0 | 95.18 | NaN | NaN | NaN |
| 1 | 1970-02 | IT | NaN | 68.2 | 44.60 | NaN | NaN | NaN |
| 2 | 1970-03 | IT | NaN | 78.8 | 29.27 | NaN | NaN | NaN |
| 3 | 1970-04 | IT | NaN | 81.4 | 22.64 | NaN | NaN | NaN |
| 4 | 1970-05 | IT | NaN | 79.9 | 20.32 | NaN | NaN | NaN |
| Number of Variables | 8 |
|---|---|
| Number of Rows | 1376 |
| Missing Cells | 3975 |
| Missing Cells (%) | 36.1% |
| Duplicate Rows | 0 |
| Duplicate Rows (%) | 0.0% |
| Total Size in Memory | 267.0 KB |
| Average Row Size in Memory | 198.7 B |
| Variable Types |
|
| obs_flag has 1217 (88.44%) missing values | Missing |
|---|---|
| adult_cattle has 171 (12.43%) missing values | Missing |
| pork has 159 (11.56%) missing values | Missing |
| lamb has 411 (29.87%) missing values | Missing |
| chicken has 932 (67.73%) missing values | Missing |
| duck has 1085 (78.85%) missing values | Missing |
| pork is skewed | Skewed |
| chicken is skewed | Skewed |
| time_period has a high cardinality: 627 distinct values | High Cardinality |
| obs_flag has constant value "c" | Constant |
| time_period has constant length 7 | Constant Length |
|---|---|
| geo has constant length 2 | Constant Length |
| obs_flag has constant length 1 | Constant Length |
categorical
| Approximate Distinct Count | 627 |
|---|---|
| Approximate Unique (%) | 45.6% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Memory Size | 96.8 KB |
| Mean | 7 |
|---|---|
| Standard Deviation | 0 |
| Median | 7 |
| Minimum | 7 |
| Maximum | 7 |
| 1st row | 1970-01 |
|---|---|
| 2nd row | 1970-02 |
| 3rd row | 1970-03 |
| 4th row | 1970-04 |
| 5th row | 1970-05 |
| Count | 0 |
|---|---|
| Lowercase Letter | 0 |
| Space Separator | 0 |
| Uppercase Letter | 0 |
| Dash Punctuation | 1376 |
| Decimal Number | 8256 |
categorical
| Approximate Distinct Count | 2 |
|---|---|
| Approximate Unique (%) | 0.1% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Memory Size | 90.0 KB |
| Mean | 2 |
|---|---|
| Standard Deviation | 0 |
| Median | 2 |
| Minimum | 2 |
| Maximum | 2 |
| 1st row | IT |
|---|---|
| 2nd row | IT |
| 3rd row | IT |
| 4th row | IT |
| 5th row | IT |
| Count | 2752 |
|---|---|
| Lowercase Letter | 0 |
| Space Separator | 0 |
| Uppercase Letter | 2752 |
| Dash Punctuation | 0 |
| Decimal Number | 0 |
categorical
| Approximate Distinct Count | 1 |
|---|---|
| Approximate Unique (%) | 0.6% |
| Missing | 1217 |
| Missing (%) | 88.4% |
| Memory Size | 10.2 KB |
| Mean | 1 |
|---|---|
| Standard Deviation | 0 |
| Median | 1 |
| Minimum | 1 |
| Maximum | 1 |
| 1st row | c |
|---|---|
| 2nd row | c |
| 3rd row | c |
| 4th row | c |
| 5th row | c |
| Count | 159 |
|---|---|
| Lowercase Letter | 159 |
| Space Separator | 0 |
| Uppercase Letter | 0 |
| Dash Punctuation | 0 |
| Decimal Number | 0 |
numerical
| Approximate Distinct Count | 974 |
|---|---|
| Approximate Unique (%) | 80.8% |
| Missing | 171 |
| Missing (%) | 12.4% |
| Infinite | 0 |
| Infinite (%) | 0.0% |
| Memory Size | 18.8 KB |
| Mean | 59.6241 |
| Minimum | 17.6 |
| Maximum | 102.06 |
| Zeros | 0 |
| Zeros (%) | 0.0% |
| Negatives | 0 |
| Negatives (%) | 0.0% |
| Minimum | 17.6 |
|---|---|
| 5-th Percentile | 25.42 |
| Q1 | 43.8 |
| Median | 57.8 |
| Q3 | 78.24 |
| 95-th Percentile | 89.428 |
| Maximum | 102.06 |
| Range | 84.46 |
| IQR | 34.44 |
| Mean | 59.6241 |
|---|---|
| Standard Deviation | 20.5594 |
| Variance | 422.6876 |
| Sum | 71847.02 |
| Skewness | -0.1301 |
| Kurtosis | -1.1316 |
| Coefficient of Variation | 0.3448 |
numerical
| Approximate Distinct Count | 890 |
|---|---|
| Approximate Unique (%) | 73.1% |
| Missing | 159 |
| Missing (%) | 11.6% |
| Infinite | 0 |
| Infinite (%) | 0.0% |
| Memory Size | 19.0 KB |
| Mean | 62.9609 |
| Minimum | 6.3 |
| Maximum | 179.53 |
| Zeros | 0 |
| Zeros (%) | 0.0% |
| Negatives | 0 |
| Negatives (%) | 0.0% |
| Minimum | 6.3 |
|---|---|
| 5-th Percentile | 11.1 |
| Q1 | 17.19 |
| Median | 33 |
| Q3 | 113.23 |
| 95-th Percentile | 139.338 |
| Maximum | 179.53 |
| Range | 173.23 |
| IQR | 96.04 |
| Mean | 62.9609 |
|---|---|
| Standard Deviation | 50.0873 |
| Variance | 2508.7419 |
| Sum | 76623.41 |
| Skewness | 0.3616 |
| Kurtosis | -1.5441 |
| Coefficient of Variation | 0.7955 |
numerical
| Approximate Distinct Count | 401 |
|---|---|
| Approximate Unique (%) | 41.5% |
| Missing | 411 |
| Missing (%) | 29.9% |
| Infinite | 0 |
| Infinite (%) | 0.0% |
| Memory Size | 15.1 KB |
| Mean | 3.5855 |
| Minimum | 0.53 |
| Maximum | 15.66 |
| Zeros | 0 |
| Zeros (%) | 0.0% |
| Negatives | 0 |
| Negatives (%) | 0.0% |
| Minimum | 0.53 |
|---|---|
| 5-th Percentile | 0.91 |
| Q1 | 1.57 |
| Median | 3.2 |
| Q3 | 4.8 |
| 95-th Percentile | 7.7 |
| Maximum | 15.66 |
| Range | 15.13 |
| IQR | 3.23 |
| Mean | 3.5855 |
|---|---|
| Standard Deviation | 2.36 |
| Variance | 5.5696 |
| Sum | 3460.04 |
| Skewness | 1.4111 |
| Kurtosis | 3.1575 |
| Coefficient of Variation | 0.6582 |
numerical
| Approximate Distinct Count | 408 |
|---|---|
| Approximate Unique (%) | 91.9% |
| Missing | 932 |
| Missing (%) | 67.7% |
| Infinite | 0 |
| Infinite (%) | 0.0% |
| Memory Size | 6.9 KB |
| Mean | 42.5796 |
| Minimum | 0.58 |
| Maximum | 97.64 |
| Zeros | 0 |
| Zeros (%) | 0.0% |
| Negatives | 0 |
| Negatives (%) | 0.0% |
| Minimum | 0.58 |
|---|---|
| 5-th Percentile | 7.5015 |
| Q1 | 9.3975 |
| Median | 51.925 |
| Q3 | 75.145 |
| 95-th Percentile | 87.7215 |
| Maximum | 97.64 |
| Range | 97.06 |
| IQR | 65.7475 |
| Mean | 42.5796 |
|---|---|
| Standard Deviation | 33.0809 |
| Variance | 1094.3473 |
| Sum | 18905.35 |
| Skewness | 0.113 |
| Kurtosis | -1.7724 |
| Coefficient of Variation | 0.7769 |
numerical
| Approximate Distinct Count | 62 |
|---|---|
| Approximate Unique (%) | 21.3% |
| Missing | 1085 |
| Missing (%) | 78.8% |
| Infinite | 0 |
| Infinite (%) | 0.0% |
| Memory Size | 4.5 KB |
| Mean | 0.3305 |
| Minimum | 0.03 |
| Maximum | 0.8 |
| Zeros | 0 |
| Zeros (%) | 0.0% |
| Negatives | 0 |
| Negatives (%) | 0.0% |
| Minimum | 0.03 |
|---|---|
| 5-th Percentile | 0.135 |
| Q1 | 0.25 |
| Median | 0.3 |
| Q3 | 0.395 |
| 95-th Percentile | 0.61 |
| Maximum | 0.8 |
| Range | 0.77 |
| IQR | 0.145 |
| Mean | 0.3305 |
|---|---|
| Standard Deviation | 0.1411 |
| Variance | 0.01991 |
| Sum | 96.18 |
| Skewness | 1.1061 |
| Kurtosis | 1.4954 |
| Coefficient of Variation | 0.4269 |
| time_period | year | month | geo | obs_flag | adult_cattle | pork | lamb | chicken | duck | |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1970-01 | 1970 | 01 | IT | <NA> | 76.00 | 95.18 | NaN | NaN | NaN |
| 1 | 1970-02 | 1970 | 02 | IT | <NA> | 68.20 | 44.60 | NaN | NaN | NaN |
| 2 | 1970-03 | 1970 | 03 | IT | <NA> | 78.80 | 29.27 | NaN | NaN | NaN |
| 3 | 1970-04 | 1970 | 04 | IT | <NA> | 81.40 | 22.64 | NaN | NaN | NaN |
| 4 | 1970-05 | 1970 | 05 | IT | <NA> | 79.90 | 20.32 | NaN | NaN | NaN |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 1371 | 2022-02 | 2022 | 02 | IE | NaN | 48.88 | 28.80 | 4.62 | 11.22 | NaN |
| 1372 | 2022-02 | 2022 | 02 | IE | c | NaN | NaN | NaN | NaN | NaN |
| 1373 | 2022-02 | 2022 | 02 | IT | NaN | 51.72 | 102.22 | 1.01 | 64.32 | 0.16 |
| 1374 | 2022-03 | 2022 | 03 | IE | NaN | 52.07 | 30.83 | 5.05 | 10.80 | NaN |
| 1375 | 2022-03 | 2022 | 03 | IE | c | NaN | NaN | NaN | NaN | NaN |
1376 rows × 10 columns
[Text(0.5, 1.0, 'Duck (in tonnes) by year')]
[Text(0.5, 1.0, 'Chicken (in tonnes) by year')]
[Text(0.5, 1.0, 'Lamb (in tonnes) by year')]
[Text(0.5, 1.0, 'Pork (in tonnes) by year')]
[Text(0.5, 1.0, 'Adult cattle (in tonnes) by year')]
<AxesSubplot:>
<AxesSubplot:>
| time_period | year | month | geo | obs_flag | adult_cattle | pork | lamb | chicken | duck | |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1970-01 | 1970 | 01 | IT | <NA> | 76.0 | 95.18 | NaN | NaN | NaN |
| 1 | 1970-02 | 1970 | 02 | IT | <NA> | 68.2 | 44.60 | NaN | NaN | NaN |
| 2 | 1970-03 | 1970 | 03 | IT | <NA> | 78.8 | 29.27 | NaN | NaN | NaN |
| 3 | 1970-04 | 1970 | 04 | IT | <NA> | 81.4 | 22.64 | NaN | NaN | NaN |
| 4 | 1970-05 | 1970 | 05 | IT | <NA> | 79.9 | 20.32 | NaN | NaN | NaN |
| time_period | year | month | geo | adult_cattle | pork | lamb | chicken | |
|---|---|---|---|---|---|---|---|---|
| 0 | 2004-01-01 | 2004 | 01 | IE | 42.40 | 18.60 | 4.10 | 9.01 |
| 1 | 2004-02-01 | 2004 | 02 | IE | 40.10 | 15.90 | 3.10 | 8.17 |
| 2 | 2004-03-01 | 2004 | 03 | IE | 45.60 | 18.00 | 3.40 | 9.48 |
| 3 | 2004-04-01 | 2004 | 04 | IE | 42.60 | 17.30 | 4.50 | 8.14 |
| 4 | 2004-05-01 | 2004 | 05 | IE | 37.10 | 15.40 | 5.20 | 7.82 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 214 | 2021-11-01 | 2021 | 11 | IE | 56.10 | 31.48 | 4.99 | 10.83 |
| 215 | 2021-12-01 | 2021 | 12 | IE | 45.54 | 27.03 | 5.05 | 12.12 |
| 216 | 2022-01-01 | 2022 | 01 | IE | 47.31 | 28.01 | 4.32 | 11.31 |
| 217 | 2022-02-01 | 2022 | 02 | IE | 48.88 | 28.80 | 4.62 | 11.22 |
| 218 | 2022-03-01 | 2022 | 03 | IE | 52.07 | 30.83 | 5.05 | 10.80 |
219 rows × 8 columns
| time_period | year | month | geo | adult_cattle | pork | lamb | chicken | |
|---|---|---|---|---|---|---|---|---|
| 118 | 2013-11-01 | 2013 | 11 | IE | 47.26 | 20.15 | 4.45 | NaN |
| 119 | 2013-12-01 | 2013 | 12 | IE | 41.03 | 18.35 | 4.00 | NaN |
| 130 | 2014-11-01 | 2014 | 11 | IE | 48.11 | 22.51 | 3.72 | NaN |
| 131 | 2014-12-01 | 2014 | 12 | IE | 46.53 | 21.57 | 3.91 | NaN |
| 132 | 2015-01-01 | 2015 | 01 | IE | 47.16 | 23.29 | 3.50 | NaN |
| 133 | 2015-02-01 | 2015 | 02 | IE | 45.20 | 21.65 | 2.98 | NaN |
time_period year month geo adult_cattle pork lamb chicken 117 2013-10-01 2013 10 IE 51.50 20.66 5.01 9.36 118 2013-11-01 2013 11 IE 47.26 20.15 4.45 NaN 119 2013-12-01 2013 12 IE 41.03 18.35 4.00 NaN 120 2014-01-01 2014 01 IE 49.31 22.11 3.58 9.13 121 2014-02-01 2014 02 IE 45.14 19.10 3.10 7.79 122 2014-03-01 2014 03 IE 48.20 19.83 2.93 7.71 123 2014-04-01 2014 04 IE 49.13 20.84 3.58 7.68 124 2014-05-01 2014 05 IE 48.69 20.55 3.63 9.77 125 2014-06-01 2014 06 IE 46.12 19.81 4.37 7.88 126 2014-07-01 2014 07 IE 45.74 21.86 5.05 8.02 127 2014-08-01 2014 08 IE 46.26 20.14 4.57 7.87 128 2014-09-01 2014 09 IE 53.53 22.68 5.18 7.80 129 2014-10-01 2014 10 IE 53.89 23.17 4.96 9.81 130 2014-11-01 2014 11 IE 48.11 22.51 3.72 NaN 131 2014-12-01 2014 12 IE 46.53 21.57 3.91 NaN 132 2015-01-01 2015 01 IE 47.16 23.29 3.50 NaN 133 2015-02-01 2015 02 IE 45.20 21.65 2.98 NaN
time_period year month geo adult_cattle pork lamb chicken 117 2013-10-01 2013 10 IE 51.50 20.66 5.01 9.36 118 2013-11-01 2013 11 IE 47.26 20.15 4.45 9.28 119 2013-12-01 2013 12 IE 41.03 18.35 4.00 9.21 120 2014-01-01 2014 01 IE 49.31 22.11 3.58 9.13 121 2014-02-01 2014 02 IE 45.14 19.10 3.10 7.79 122 2014-03-01 2014 03 IE 48.20 19.83 2.93 7.71 123 2014-04-01 2014 04 IE 49.13 20.84 3.58 7.68 124 2014-05-01 2014 05 IE 48.69 20.55 3.63 9.77 125 2014-06-01 2014 06 IE 46.12 19.81 4.37 7.88 126 2014-07-01 2014 07 IE 45.74 21.86 5.05 8.02 127 2014-08-01 2014 08 IE 46.26 20.14 4.57 7.87 128 2014-09-01 2014 09 IE 53.53 22.68 5.18 7.80 129 2014-10-01 2014 10 IE 53.89 23.17 4.96 9.81 130 2014-11-01 2014 11 IE 48.11 22.51 3.72 9.52 131 2014-12-01 2014 12 IE 46.53 21.57 3.91 9.23 132 2015-01-01 2015 01 IE 47.16 23.29 3.50 8.95 133 2015-02-01 2015 02 IE 45.20 21.65 2.98 8.66
| time_period | year | month | geo | adult_cattle | pork | lamb | chicken | |
|---|---|---|---|---|---|---|---|---|
| 0 | 2004-01-01 | 2004 | 01 | IT | 89.80 | 151.10 | 3.20 | 57.51 |
| 1 | 2004-02-01 | 2004 | 02 | IT | 70.56 | 138.91 | 3.17 | 55.17 |
| 2 | 2004-03-01 | 2004 | 03 | IT | 87.10 | 139.80 | 3.00 | 61.57 |
| 3 | 2004-04-01 | 2004 | 04 | IT | 75.70 | 135.00 | 5.30 | 59.35 |
| 4 | 2004-05-01 | 2004 | 05 | IT | 79.40 | 129.60 | 1.50 | 57.55 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 213 | 2021-10-01 | 2021 | 10 | IT | 55.91 | 100.25 | 1.55 | 91.51 |
| 214 | 2021-11-01 | 2021 | 11 | IT | 57.87 | 114.05 | 1.43 | 86.15 |
| 215 | 2021-12-01 | 2021 | 12 | IT | 66.43 | 127.13 | 3.99 | 83.31 |
| 216 | 2022-01-01 | 2022 | 01 | IT | 51.38 | 104.42 | 1.55 | 79.44 |
| 217 | 2022-02-01 | 2022 | 02 | IT | 51.72 | 102.22 | 1.01 | 64.32 |
218 rows × 8 columns
<AxesSubplot:xlabel='month', ylabel='adult_cattle'>
<AxesSubplot:xlabel='month', ylabel='adult_cattle'>
| adult_cattle | pork | lamb | chicken | |
|---|---|---|---|---|
| count | 219.000000 | 219.000000 | 219.000000 | 219.000000 |
| mean | 47.404977 | 21.043425 | 4.298904 | 9.568082 |
| std | 5.973172 | 4.060273 | 0.863420 | 1.874170 |
| min | 26.800000 | 14.100000 | 2.460000 | 6.260000 |
| 25% | 43.215000 | 17.400000 | 3.665000 | 7.975000 |
| 50% | 47.200000 | 20.320000 | 4.320000 | 9.360000 |
| 75% | 51.440000 | 24.250000 | 4.955000 | 10.780000 |
| max | 61.780000 | 31.480000 | 6.600000 | 14.860000 |
| adult_cattle | pork | lamb | chicken | |
|---|---|---|---|---|
| count | 218.000000 | 218.000000 | 218.000000 | 218.000000 |
| mean | 67.347385 | 125.980275 | 2.077890 | 74.226468 |
| std | 13.027080 | 14.050157 | 2.009891 | 11.623118 |
| min | 42.660000 | 82.020000 | 0.530000 | 43.850000 |
| 25% | 56.795000 | 117.057500 | 1.050000 | 65.792500 |
| 50% | 64.860000 | 127.595000 | 1.320000 | 75.530000 |
| 75% | 76.872500 | 134.455000 | 2.492500 | 83.737500 |
| max | 96.790000 | 179.530000 | 13.240000 | 97.640000 |
0.24992133006147554
0.06741469787829235
0.31
Ireland - confidence intervals for adult cattle
count 44.000000 mean 46.202500 std 6.520426 min 32.940000 25% 40.832500 50% 46.395000 75% 51.500000 max 59.900000 Name: adult_cattle, dtype: float64
The variance of adult cattle production in Ireland is: 42.51595872093023
The 90% confidence interval for the mean of production in tonnes for adult cattle in Ireland is: (44.55002221669012, 47.854977783309884)
Italy - confidence intervals for adult cattle
count 44.000000 mean 68.477500 std 12.483928 min 50.430000 25% 58.115000 50% 64.300000 75% 76.770000 max 90.150000 Name: adult_cattle, dtype: float64
The variance of adult cattle production in Italy is: 155.84846569767444
The 90% confidence interval for the mean of production in tonnes for adult cattle in Italy is: (65.31368626935625, 71.6413137306437)
| meat | geo | TIME_PERIOD | OBS_VALUE | OBS_FLAG | |
|---|---|---|---|---|---|
| 0 | B1200 | IE | 1974-01 | 26.0 | <NA> |
| 1 | B1200 | IE | 1974-02 | 22.8 | <NA> |
| 2 | B1200 | IE | 1974-03 | 20.4 | <NA> |
| 3 | B1200 | IE | 1974-04 | 18.5 | <NA> |
| 4 | B1200 | IE | 1974-05 | 22.3 | <NA> |
meat object geo object TIME_PERIOD object OBS_VALUE float64 OBS_FLAG object dtype: object
Legend:\
B1200 = Adult cattle
B3100 = Pork
B4110 = Lamb
| meat | geo | TIME_PERIOD | OBS_VALUE | OBS_FLAG | year | |
|---|---|---|---|---|---|---|
| 360 | B1200 | IE | 2004-01 | 42.4 | NaN | 2004 |
| 361 | B1200 | IE | 2004-02 | 40.1 | NaN | 2004 |
| 362 | B1200 | IE | 2004-03 | 45.6 | NaN | 2004 |
| 363 | B1200 | IE | 2004-04 | 42.6 | NaN | 2004 |
| 364 | B1200 | IE | 2004-05 | 37.1 | NaN | 2004 |
Test to be performed in the following features:
B1200 = Adult cattle
B3100 = Pork
B4110 = Lamb
Now we set 3 hypotheses tests to verify the distribution of the Irish subset for each type of meat.
H0: Sample of adult cattle is a normal distributions (p>0.05).\ H1: Sample of adult cattle is not a normal distributions.
H0: Sample of pork is a normal distributions (p>0.05).\ H1: Sample of pork is not a normal distributions.
H0: Sample of lamb is a normal distributions (p>0.05).\ H1: Sample of lamb is not a normal distributions.
B1200: ShapiroResult(statistic=0.9598056077957153, pvalue=0.1756906658411026) B3100: ShapiroResult(statistic=0.9293535947799683, pvalue=0.004235260654240847) B4110: ShapiroResult(statistic=0.9693857431411743, pvalue=0.3440837562084198)
The result above shows that only PORK is not of a normal distribution, p value is less than 0.05.
H0: Sample of adult cattle is a normal distributions (p>0.05). ACCEPTED\ H1: Sample of adult cattle is not a normal distributions. REJECTED
H0: Sample of pork is a normal distributions (p>0.05). REJECTED\ H1: Sample of pork is not a normal distributions. ACCEPTED
H0: Sample of lamb is a normal distributions (p>0.05). ACCEPTED\ H1: Sample of lamb is not a normal distributions. REJECTED
Defining the hypothesis:
H0: The median is equal across all types of meat.(p>0.05)
H1: The median is not equal across all types of meat.(p<0.05)
KruskalResult(statistic=114.49404563309356, pvalue=1.3738328143010203e-25)
Results of the hypothesis:
H0: The median is equal across all types of meat.(p>0.05) REJECTED
H1: The median is not equal across all types of meat.(p<0.05) ACCEPTED
Test to be performed in the following features:
B1200 = Adult cattle
B3100 = Pork
B4110 = Lamb
Now we set 3 hypotheses tests to verify the distribution of the Italian subset for each type of meat.
H0: Sample of adult cattle is a normal distributions (p>0.05).\ H1: Sample of adult cattle is not a normal distributions.
H0: Sample of pork is a normal distributions (p>0.05).\ H1: Sample of pork is not a normal distributions.
H0: Sample of lamb is a normal distributions (p>0.05).\ H1: Sample of lamb is not a normal distributions.
B1200: ShapiroResult(statistic=0.9233238697052002, pvalue=0.00872541218996048) B3100: ShapiroResult(statistic=0.9870803356170654, pvalue=0.8632708191871643) B4110: ShapiroResult(statistic=0.7738800048828125, pvalue=1.609310629646643e-06)
The result above shows that only PORK has a normal distribution, p value is greater than 0.05.
H0: Sample of adult cattle is a normal distributions (p>0.05). REJECTED\ H1: Sample of adult cattle is not a normal distributions. ACCEPTED
H0: Sample of pork is a normal distributions (p>0.05). ACCEPTED\ H1: Sample of pork is not a normal distributions. REJECTED
H0: Sample of lamb is a normal distributions (p>0.05). REJECTED\ H1: Sample of lamb is not a normal distributions. ACCEPTED
Defining the hypothesis:
H0: The median is equal across all types of meat.(p>0.05)
H1: The median is not equal across all types of meat.(p<0.05)
KruskalResult(statistic=115.17281856337199, pvalue=9.784535569041728e-26)
Results of the hypothesis:
H0: The median is equal across all types of meat.(p>0.05) REJECTED
H1: The median is not equal across all types of meat.(p<0.05) ACCEPTED
Here we will use Levene’s test to check whether the variance across both samples of adult cattle from Ireland and Italy are the same.
The Levene test is a good alternative to the Bartlett's test. In this case, we know that one of the samples does not have a normal distribution (adult cattle in the Italian dataset) so the Levene is a better test to perform as Bartlett's test is sensitive to normality in the sample.
Therefore we will use the adult cattle production to perform Levene's test.
Defining the hypothesis:
H0: The adult cattle groups have equal variance between both countries.(p>0.05)
H1: The adult cattle groups have different variances when comparing both countries. (p<0.05)
LeveneResult(statistic=29.818075167975234, pvalue=5.458941325858164e-07)
Concluding the hypothesis:
H0: The adult cattle groups have equal variance between both countries.(p>0.05) REJECTED
H1: The adult cattle groups have different variances when comparing both countries. (p<0.05) ACCEPTED
LinearRegression()
[[45.44 39.2 ] [42.01 41.17] [53.87 52.04] [42.88 38.1 ] [40.02 41.11] [45.01 41.03] [50.6 53.29] [49.31 51.5 ] [49.48 43.7 ] [52.36 54.97] [54.44 59.9 ] [49.56 32.94] [43. 37.36] [41.34 41.71] [44.51 48.27] [48.35 45.64] [41.66 37.95] [47.08 53.12] [52.34 56.06] [48.75 49.2 ] [44.76 37.69] [40.35 40.24] [44.01 42.26] [50.05 54.98] [49.43 48.17] [46.92 46.53] [45.41 38.11] [48.19 46.26] [51.85 52.86] [46.01 44.5 ] [47.82 38.41] [43.9 51.5 ] [44.25 45.34] [50.71 53.89] [51.21 48.2 ] [47.05 50.75] [48.62 56.3 ] [45.3 48.69] [48.58 37.1 ] [46.9 47.44] [46.45 51.25] [47.67 48.48] [41.2 40.1 ] [43.04 45.6 ]]
0.39138344687831716
array([ 0.15, -0.03, 0.36, -0.85, 0.04, 0.29, 0.03, 0.58, 0.29,
0.13])
0.0
1.0
3.820953738998987
LinearRegression()
[[41.29 39.2 ] [41.22 41.17] [56.48 52.04] [36.79 38.1 ] [45.08 41.11] [44.77 41.03] [51.87 53.29] [51.29 51.5 ] [49.87 43.7 ] [53.54 54.97] [59.38 59.9 ] [48.65 32.94] [44.27 37.36] [43.49 41.71] [43.04 48.27] [47.81 45.64] [45.62 37.95] [46.57 53.12] [53.31 56.06] [49.39 49.2 ] [45.19 37.69] [39.16 40.24] [42.98 42.26] [49.36 54.98] [49.67 48.17] [46.91 46.53] [39.66 38.11] [48.81 46.26] [51.79 52.86] [45.97 44.5 ] [48.78 38.41] [43.21 51.5 ] [42.39 45.34] [52.46 53.89] [52.98 48.2 ] [44.33 50.75] [49.12 56.3 ] [44.89 48.69] [48.04 37.1 ] [45.7 47.44] [43.21 51.25] [46.79 48.48] [40.48 40.1 ] [43.12 45.6 ]]
0.36839779629277236
3.822812590118826
Fitting 5 folds for each of 20 candidates, totalling 100 fits
GridSearchCV(cv=5, estimator=ElasticNet(),
param_grid={'alpha': [0.1, 1, 5, 10, 100],
'l1_ratio': [0.1, 0.7, 0.99, 1]},
scoring='neg_mean_squared_error', verbose=1)
{'alpha': 0.1, 'l1_ratio': 0.7}
3.856235930999462
5.17863675618009
ElasticNetCV(l1_ratio=[0.99], max_iter=1000000, n_alphas=1)
0.010861502377241808
[[45.85 39.2 ] [42.82 41.17] [55.24 52.04] [43.64 38.1 ] [41.53 41.11] [45.98 41.03] [51.31 53.29] [49.99 51.5 ] [49.78 43.7 ] [52.79 54.97] [55.14 59.9 ] [50.5 32.94] [43.93 37.36] [42.64 41.71] [45.37 48.27] [48.62 45.64] [43.11 37.95] [47.63 53.12] [53.53 56.06] [49.68 49.2 ] [45.38 37.69] [41.25 40.24] [45.02 42.26] [51.25 54.98] [50.19 48.17] [47.61 46.53] [45.94 38.11] [48.59 46.26] [53.12 52.86] [46.56 44.5 ] [48.53 38.41] [45.04 51.5 ] [45.46 45.34] [51.29 53.89] [51.74 48.2 ] [47.97 50.75] [49.24 56.3 ] [46.25 48.69] [49.63 37.1 ] [47.59 47.44] [47.35 51.25] [48.62 48.48] [42.23 40.1 ] [44.18 45.6 ]]
0.35454920041954907
3.856235930999462
Ridge()
25.290130793298598
0.3913280060377118
3.8181494493417842
RandomForestRegressor()
0.3621379632263666
3.9046818181818126
0.023982203868910257
LinearRegression()
[[79.36 67.6 ] [59.56 58.15] [51.43 51.58] [53.41 53.83] [76.35 80.67] [73.11 62.96] [71.71 84.07] [88.83 90.02] [66.06 50.43] [67.09 58.01] [64.52 55.17] [72.82 85.28] [76.47 73.94] [81.38 90.15] [85.58 86.78] [59.62 61.01] [52.82 57.47] [56.56 59.27] [56.04 66.48] [70.67 57.65] [67.17 60.07] [61.71 57.12] [67.61 74.31] [76.53 75.47] [70.87 74.72] [75.37 63.38] [45.97 52.08] [71.44 85.94] [89.88 86.29] [61.16 62.37] [81.13 86.2 ] [62.77 58.86] [60.48 62.6 ] [75.07 74.03] [64.48 55.77] [74.04 89.18] [73.6 64.63] [75.85 71.49] [71.13 74.35] [61.24 63.97] [50.25 53.14] [84.18 70.56] [80.48 87.1 ] [66.12 58.86]]
0.600467979100257
array([-0.54, -0.35, 0.17, -0.89, -0.74, -1.98, -3.95, -2.9 , -0.96,
-0.94])
0.0
1.0
6.349031500181686
LinearRegression()
[[73.68 67.6 ] [55.49 58.15] [53.91 51.58] [53.17 53.83] [79.36 80.67] [71.15 62.96] [73.4 84.07] [90.1 90.02] [67.18 50.43] [64.94 58.01] [61.58 55.17] [74.94 85.28] [79.67 73.94] [87.65 90.15] [98.16 86.78] [56.48 61.01] [51.63 57.47] [54.11 59.27] [52.24 66.48] [67.24 57.65] [63.77 60.07] [58.06 57.12] [68.01 74.31] [79.47 75.47] [70.86 74.72] [76.32 63.38] [61.66 52.08] [73.57 85.94] [75.71 86.29] [58.06 62.37] [80.52 86.2 ] [59.29 58.86] [57.35 62.6 ] [78.05 74.03] [61.47 55.77] [77.37 89.18] [75.71 64.63] [76.7 71.49] [73.37 74.35] [57.84 63.97] [54.03 53.14] [86.71 70.56] [85.66 87.1 ] [60.53 58.86]]
0.6143036897569417
6.281737502431714
Fitting 5 folds for each of 20 candidates, totalling 100 fits
GridSearchCV(cv=5, estimator=ElasticNet(),
param_grid={'alpha': [0.1, 1, 5, 10, 100],
'l1_ratio': [0.1, 0.7, 0.99, 1]},
scoring='neg_mean_squared_error', verbose=1)
{'alpha': 0.1, 'l1_ratio': 0.7}
6.632139606296655
7.918201011455562
ElasticNetCV(l1_ratio=[0.99], max_iter=1000000, n_alphas=1)
0.10437953194916531
[[77.83 67.6 ] [57.92 58.15] [49.73 51.58] [51.71 53.83] [74.64 80.67] [71.57 62.96] [69.87 84.07] [85.53 90.02] [64.38 50.43] [65.43 58.01] [62.88 55.17] [71.14 85.28] [74.69 73.94] [79.41 90.15] [82.52 86.78] [58.03 61.01] [51.08 57.47] [54.95 59.27] [54.31 66.48] [68.53 57.65] [65.32 60.07] [60.14 57.12] [65.95 74.31] [74.59 75.47] [68.78 74.72] [73.24 63.38] [44.26 52.08] [69.73 85.94] [87.81 86.29] [59.16 62.37] [79.15 86.2 ] [61.03 58.86] [58.91 62.6 ] [73.35 74.03] [62.85 55.77] [72.27 89.18] [71.74 64.63] [73.97 71.49] [69.52 74.35] [59.58 63.97] [48.52 53.14] [82.18 70.56] [78.55 87.1 ] [63.95 58.86]]
0.5883437296795021
6.632139606296655
Ridge()
60.80501719458735
0.6007718967050969
6.336275272655933
RandomForestRegressor()
0.6362091466733001
5.702834090909089
-1.5020808483933574
['It would be “kinder to shoot” the hundreds of thousands of unwanted male dairy calves due to be born in Ireland this year, rather than export them to the Middle East or let them die on the farm, experts have told the Guardian.', 'Irish farmers have hit a streak of gold on dairy exports, and as a result the industry has rapidly expanded, with the national dairy herd rising from about 1m in 2010 to 1.6m this year.', 'But that creates a whole new problem.', 'Dairy cows generally give birth every year in order to maintain their milk output.', 'But male dairy calves are no use to the farmer as they cannot produce milk, which means that Ireland will need to deal with as many as 800,000 unwanted male calves this year, in what has been described by the Irish agricultural press as a “calf tsunami”.', 'Q&A Why are we reporting on live exports?', 'This week the Guardian’s Animals Farmed series is focusing on the global live animal export trade, which, despite welfare and disease concerns, has quadrupled over the last fifty years.', 'Nearly 2 billion animals a year are loaded onto trucks or ships and sent off to new countries on journeys that can take weeks.', 'Every day at least 5 million creatures are in transit, in a secretive global trade in live farm animals.', 'And those numbers are just the cross-border journeys.', 'They do not include long journeys within countries, which are becoming more frequent due to a trend that has seen smaller slaughterhouses close down.', 'We’re taking a moment to focus on some of the implications of this global trade.', 'The situation is further complicated by Ireland’s “compact calving” system, which sees most calves born in a 12-week period from February to April, and the swift removal of calves from their mothers.', 'Both increase the need for extra short-term labour, which can be costly or hard to find.', 'The Irish beef trade has formerly been a useful outlet for unwanted dairy calves, but demand last year was sluggish, and selective breeding increasingly means that dairy calves may not be suitable for beef anyway.', 'Exporting the calves is another option.', 'Although campaigners are opposed to the trade, the Irish government has described it as “a vital safety valve for the beef sector”.', 'The only alternative is to shoot the calf at birth, or to let calves die of neglect.', 'In 2015–16 New Zealand, another country with a large dairy industry, was hit by the male calf scandal when videos and reports of starved and abused male dairy calves were widely circulated by welfare organisations.', '“The danger of having so many calves in 2020, on top of the existing pressures, means there is an emergency welfare case to be made, although no one wants to talk about it, for killing the calves on a one-off basis at three or four days old,” said a senior figure in Ireland’s state agricultural network.', 'The source spoke to the Guardian on condition of anonymity for fear they might lose their job.', '“We know there is going to be suffering.', 'We know the farmers can’t manage and we need to offer a realistic solution to ease that suffering, for the calves and the \nfarmers,” the source said.', '“Vets often say they are only called to treat female calves.', 'The whole reason New Zealand is in the situation of killing calves at four days old is because they \nhad the same problems with valueless male dairy calves about four years ago.', '“Vets are not called to sick pigs [in Ireland],” the source added.', '“And this is the way we are going with male calves.” Calves and pigs are worth significantly \nless than the cost of a vet, so it can be cheaper to simply let them die.', 'Difficult as the decision is, the source said it was preferable to letting them “die in their own scour [diarrhoea], which is what is happening now and we know \nis going to get worse”.', 'Although most dairy industry professionals refused to openly discuss the possibility of a cull, Cork based cattle vet Bill Cashman spoke on the record.', '“When a male calf has no value, or is a net cost, with the best will in the world a farmer, especially when under intense day and night work [pressure], will prioritise female calf care over the bulls.”\n\nAlthough Cashman said it was a “wanton waste” to kill any animal at birth, the immediate priority was to ensure that “males are not suffering from a \nlack of care and disease” or spreading those diseases, and “that means a cull at one or two days old.', 'I would not leave them longer.”\n\nAsked how a cull might work, Cashman said, in line with the Federation of Veterinarians of Europe policy, he would prefer to see it happen on the farm,\nto avoid transport, by lethal injection.', '“It’s instantaneous.', 'A visit could be made every second day [during calving season.', ']”\n\nThe option of exporting the calves may provide a financial return for farmers, but campaigners have queried the welfare implications of sending young,\nunweaned calves on long journeys by ship or truck.', 'The minimum age is supposed to be 14 days, but the same source told the Guardian that it looks as if some farmers are registering calves as born earlier, in an attempt to get them off the farm as quickly as possible.', 'European veal farms are the main export destination for younger calves, while older animals – male and female – tend to sell to Turkey, Libya, Morocco, \nLebanon, Russia, Tunisia, Rwanda or Kosovo for beef and dairy use.', 'Mistreatment has been well documented.', 'In a 2018 report on farm animal welfare the European Court of Auditors noted Ireland was guilty of infringing calf \ntransport rules.', 'A 2016 investigation by Compassion in World Farming showed that some journeys were lasting over 27 hours, in cramped conditions with no drinking facilities.', 'In May 2019footage taken at a at a French control post appeared in Cherbourg by animal activist group Eyes on Animals appeared to show calf mistreatment.', 'There is also concern about slaughter conditions in non-EU countries.', 'But the Irish government has repeatedly pledged its support for the export sector and has encouraged companies to form their own representative organisation.', 'In January 2017, Ireland’s minister for the Department of Agriculture, Food and the Marine (DAFM), Michael Creed, reportedly told the Irish Farmers’ Association annual meeting that the live export trade was “critically important”.', 'There has been a rise in the number of cattle exported over recent years although the value of industry for livestock – at about €110m (£94m) a year – has declined 6% year-on-year.', '“It is not boosting the economy,” Caroline Rowley, from Compassion in World Farming, said.', '“It is not stimulating prices, live export volumes are going up each year \nbut factory prices are not following.', 'It is not helping the average livestock farmer.', 'The only winners in this outdated industry are the exporters.”\n\nColorado-based animal science expert Dr Mary Temple Grandin told the Guardian that from a welfare perspective slaughtering very young calves is likely more humane \nthan a long journey by sea without milk replacer.', 'The drawback, she said, was a potential food waste issue.', 'Asked what happens to their slaughtered calves, New Zealand’s \nMinistry for Primary Industries said in an email they are “typically processed for pet food”, which goes some way towards addressing Grandin’s waste concerns.', 'Although no farmer would endorse the idea of an emergency calf cull on the record, there was agreement on individual calf neglect issues.', 'Denis Drennan is a dairy \nfarmer and committee chairman with the Irish Creamery Milk Supplier Association.', 'Drennan, who has 60 dairy cows, summed up the reaction of many farmers to the idea of a cull,\nsaying if he was shooting animals at birth, he was not a farmer.', 'Asked if male calves are sometimes left to die, or whether there are farmers who only call a vet for a female calf, Drennan said yes.', 'Where a vet might cost about €150 at night, domestic prices for calves, which can earn farmers anything from €30 to €80 per head at auction, \nreportedly fell as low as €0.50 per head last year.', 'Live exporters, meanwhile, estimate their 2020 prices would stay in the €30 to €60 range.', 'Replying to questions about a potential emergency calf cull Ireland’s DAFM said by email that it “continues to engage with and work closely with \nstakeholders in relation to the incremental increase in the number of calves born on the dairy farms over the past few years, with a focus on rearing, \nmanagement and market opportunities for such calves, including opportunities for intra-community trade to other EU member states.” The department’s statement \nwent on to say that “in addition to its direct oversight of animals presented for export”, it will “continue its range of inspections and controls on farms and at \nsales to monitor compliance with animal welfare standards”.', 'One result of that engagement appears to be a December DAFM announcement that Irish government veterinary inspectors “will accompany calves from Ireland to the \ncontrol posts in Cherbourg”, without prior warning.', 'It said too that rubber teat water drinkers will be mandatory for unweaned calves from 1 December 2020.', 'Reacting to the new rules, Eyes on Animals’ Nicola Glen said she was pleased to see the “serious welfare issues associated with the transport of Irish calves” were \nfinally receiving proper attention.', 'However, she said it was unclear how the vet checks would work in practice and that although the new rubber teat rule was an \nimprovement on the existing system of hard-to-access metal pipes, December 2020 was too far away.', 'Eyes on Animals will continue its inspections until it sees concrete \nresults, she added.']
['It', 'would', 'be', '“', 'kinder', 'to', 'shoot', '”', 'the', 'hundreds', 'of', 'thousands', 'of', 'unwanted', 'male', 'dairy', 'calves', 'due', 'to', 'be', 'born', 'in', 'Ireland', 'this', 'year', ',', 'rather', 'than', 'export', 'them', 'to', 'the', 'Middle', 'East', 'or', 'let', 'them', 'die', 'on', 'the', 'farm', ',', 'experts', 'have', 'told', 'the', 'Guardian', '.', 'Irish', 'farmers', 'have', 'hit', 'a', 'streak', 'of', 'gold', 'on', 'dairy', 'exports', ',', 'and', 'as', 'a', 'result', 'the', 'industry', 'has', 'rapidly', 'expanded', ',', 'with', 'the', 'national', 'dairy', 'herd', 'rising', 'from', 'about', '1m', 'in', '2010', 'to', '1.6m', 'this', 'year', '.', 'But', 'that', 'creates', 'a', 'whole', 'new', 'problem', '.', 'Dairy', 'cows', 'generally', 'give', 'birth', 'every', 'year', 'in', 'order', 'to', 'maintain', 'their', 'milk', 'output', '.', 'But', 'male', 'dairy', 'calves', 'are', 'no', 'use', 'to', 'the', 'farmer', 'as', 'they', 'can', 'not', 'produce', 'milk', ',', 'which', 'means', 'that', 'Ireland', 'will', 'need', 'to', 'deal', 'with', 'as', 'many', 'as', '800,000', 'unwanted', 'male', 'calves', 'this', 'year', ',', 'in', 'what', 'has', 'been', 'described', 'by', 'the', 'Irish', 'agricultural', 'press', 'as', 'a', '“', 'calf', 'tsunami', '”', '.', 'Q', '&', 'A', 'Why', 'are', 'we', 'reporting', 'on', 'live', 'exports', '?', 'This', 'week', 'the', 'Guardian', '’', 's', 'Animals', 'Farmed', 'series', 'is', 'focusing', 'on', 'the', 'global', 'live', 'animal', 'export', 'trade', ',', 'which', ',', 'despite', 'welfare', 'and', 'disease', 'concerns', ',', 'has', 'quadrupled', 'over', 'the', 'last', 'fifty', 'years', '.', 'Nearly', '2', 'billion', 'animals', 'a', 'year', 'are', 'loaded', 'onto', 'trucks', 'or', 'ships', 'and', 'sent', 'off', 'to', 'new', 'countries', 'on', 'journeys', 'that', 'can', 'take', 'weeks', '.', 'Every', 'day', 'at', 'least', '5', 'million', 'creatures', 'are', 'in', 'transit', ',', 'in', 'a', 'secretive', 'global', 'trade', 'in', 'live', 'farm', 'animals', '.', 'And', 'those', 'numbers', 'are', 'just', 'the', 'cross-border', 'journeys', '.', 'They', 'do', 'not', 'include', 'long', 'journeys', 'within', 'countries', ',', 'which', 'are', 'becoming', 'more', 'frequent', 'due', 'to', 'a', 'trend', 'that', 'has', 'seen', 'smaller', 'slaughterhouses', 'close', 'down', '.', 'We', '’', 're', 'taking', 'a', 'moment', 'to', 'focus', 'on', 'some', 'of', 'the', 'implications', 'of', 'this', 'global', 'trade', '.', 'The', 'situation', 'is', 'further', 'complicated', 'by', 'Ireland', '’', 's', '“', 'compact', 'calving', '”', 'system', ',', 'which', 'sees', 'most', 'calves', 'born', 'in', 'a', '12-week', 'period', 'from', 'February', 'to', 'April', ',', 'and', 'the', 'swift', 'removal', 'of', 'calves', 'from', 'their', 'mothers', '.', 'Both', 'increase', 'the', 'need', 'for', 'extra', 'short-term', 'labour', ',', 'which', 'can', 'be', 'costly', 'or', 'hard', 'to', 'find', '.', 'The', 'Irish', 'beef', 'trade', 'has', 'formerly', 'been', 'a', 'useful', 'outlet', 'for', 'unwanted', 'dairy', 'calves', ',', 'but', 'demand', 'last', 'year', 'was', 'sluggish', ',', 'and', 'selective', 'breeding', 'increasingly', 'means', 'that', 'dairy', 'calves', 'may', 'not', 'be', 'suitable', 'for', 'beef', 'anyway', '.', 'Exporting', 'the', 'calves', 'is', 'another', 'option', '.', 'Although', 'campaigners', 'are', 'opposed', 'to', 'the', 'trade', ',', 'the', 'Irish', 'government', 'has', 'described', 'it', 'as', '“', 'a', 'vital', 'safety', 'valve', 'for', 'the', 'beef', 'sector', '”', '.', 'The', 'only', 'alternative', 'is', 'to', 'shoot', 'the', 'calf', 'at', 'birth', ',', 'or', 'to', 'let', 'calves', 'die', 'of', 'neglect', '.', 'In', '2015–16', 'New', 'Zealand', ',', 'another', 'country', 'with', 'a', 'large', 'dairy', 'industry', ',', 'was', 'hit', 'by', 'the', 'male', 'calf', 'scandal', 'when', 'videos', 'and', 'reports', 'of', 'starved', 'and', 'abused', 'male', 'dairy', 'calves', 'were', 'widely', 'circulated', 'by', 'welfare', 'organisations', '.', '“', 'The', 'danger', 'of', 'having', 'so', 'many', 'calves', 'in', '2020', ',', 'on', 'top', 'of', 'the', 'existing', 'pressures', ',', 'means', 'there', 'is', 'an', 'emergency', 'welfare', 'case', 'to', 'be', 'made', ',', 'although', 'no', 'one', 'wants', 'to', 'talk', 'about', 'it', ',', 'for', 'killing', 'the', 'calves', 'on', 'a', 'one-off', 'basis', 'at', 'three', 'or', 'four', 'days', 'old', ',', '”', 'said', 'a', 'senior', 'figure', 'in', 'Ireland', '’', 's', 'state', 'agricultural', 'network', '.', 'The', 'source', 'spoke', 'to', 'the', 'Guardian', 'on', 'condition', 'of', 'anonymity', 'for', 'fear', 'they', 'might', 'lose', 'their', 'job', '.', '“', 'We', 'know', 'there', 'is', 'going', 'to', 'be', 'suffering', '.', 'We', 'know', 'the', 'farmers', 'can', '’', 't', 'manage', 'and', 'we', 'need', 'to', 'offer', 'a', 'realistic', 'solution', 'to', 'ease', 'that', 'suffering', ',', 'for', 'the', 'calves', 'and', 'the', 'farmers', ',', '”', 'the', 'source', 'said', '.', '“', 'Vets', 'often', 'say', 'they', 'are', 'only', 'called', 'to', 'treat', 'female', 'calves', '.', 'The', 'whole', 'reason', 'New', 'Zealand', 'is', 'in', 'the', 'situation', 'of', 'killing', 'calves', 'at', 'four', 'days', 'old', 'is', 'because', 'they', 'had', 'the', 'same', 'problems', 'with', 'valueless', 'male', 'dairy', 'calves', 'about', 'four', 'years', 'ago', '.', '“', 'Vets', 'are', 'not', 'called', 'to', 'sick', 'pigs', '[', 'in', 'Ireland', ']', ',', '”', 'the', 'source', 'added', '.', '“', 'And', 'this', 'is', 'the', 'way', 'we', 'are', 'going', 'with', 'male', 'calves.', '”', 'Calves', 'and', 'pigs', 'are', 'worth', 'significantly', 'less', 'than', 'the', 'cost', 'of', 'a', 'vet', ',', 'so', 'it', 'can', 'be', 'cheaper', 'to', 'simply', 'let', 'them', 'die', '.', 'Difficult', 'as', 'the', 'decision', 'is', ',', 'the', 'source', 'said', 'it', 'was', 'preferable', 'to', 'letting', 'them', '“', 'die', 'in', 'their', 'own', 'scour', '[', 'diarrhoea', ']', ',', 'which', 'is', 'what', 'is', 'happening', 'now', 'and', 'we', 'know', 'is', 'going', 'to', 'get', 'worse', '”', '.', 'Although', 'most', 'dairy', 'industry', 'professionals', 'refused', 'to', 'openly', 'discuss', 'the', 'possibility', 'of', 'a', 'cull', ',', 'Cork', 'based', 'cattle', 'vet', 'Bill', 'Cashman', 'spoke', 'on', 'the', 'record', '.', '“', 'When', 'a', 'male', 'calf', 'has', 'no', 'value', ',', 'or', 'is', 'a', 'net', 'cost', ',', 'with', 'the', 'best', 'will', 'in', 'the', 'world', 'a', 'farmer', ',', 'especially', 'when', 'under', 'intense', 'day', 'and', 'night', 'work', '[', 'pressure', ']', ',', 'will', 'prioritise', 'female', 'calf', 'care', 'over', 'the', 'bulls.', '”', 'Although', 'Cashman', 'said', 'it', 'was', 'a', '“', 'wanton', 'waste', '”', 'to', 'kill', 'any', 'animal', 'at', 'birth', ',', 'the', 'immediate', 'priority', 'was', 'to', 'ensure', 'that', '“', 'males', 'are', 'not', 'suffering', 'from', 'a', 'lack', 'of', 'care', 'and', 'disease', '”', 'or', 'spreading', 'those', 'diseases', ',', 'and', '“', 'that', 'means', 'a', 'cull', 'at', 'one', 'or', 'two', 'days', 'old', '.', 'I', 'would', 'not', 'leave', 'them', 'longer.', '”', 'Asked', 'how', 'a', 'cull', 'might', 'work', ',', 'Cashman', 'said', ',', 'in', 'line', 'with', 'the', 'Federation', 'of', 'Veterinarians', 'of', 'Europe', 'policy', ',', 'he', 'would', 'prefer', 'to', 'see', 'it', 'happen', 'on', 'the', 'farm', ',', 'to', 'avoid', 'transport', ',', 'by', 'lethal', 'injection', '.', '“', 'It', '’', 's', 'instantaneous', '.', 'A', 'visit', 'could', 'be', 'made', 'every', 'second', 'day', '[', 'during', 'calving', 'season', '.', ']', '”', 'The', 'option', 'of', 'exporting', 'the', 'calves', 'may', 'provide', 'a', 'financial', 'return', 'for', 'farmers', ',', 'but', 'campaigners', 'have', 'queried', 'the', 'welfare', 'implications', 'of', 'sending', 'young', ',', 'unweaned', 'calves', 'on', 'long', 'journeys', 'by', 'ship', 'or', 'truck', '.', 'The', 'minimum', 'age', 'is', 'supposed', 'to', 'be', '14', 'days', ',', 'but', 'the', 'same', 'source', 'told', 'the', 'Guardian', 'that', 'it', 'looks', 'as', 'if', 'some', 'farmers', 'are', 'registering', 'calves', 'as', 'born', 'earlier', ',', 'in', 'an', 'attempt', 'to', 'get', 'them', 'off', 'the', 'farm', 'as', 'quickly', 'as', 'possible', '.', 'European', 'veal', 'farms', 'are', 'the', 'main', 'export', 'destination', 'for', 'younger', 'calves', ',', 'while', 'older', 'animals', '–', 'male', 'and', 'female', '–', 'tend', 'to', 'sell', 'to', 'Turkey', ',', 'Libya', ',', 'Morocco', ',', 'Lebanon', ',', 'Russia', ',', 'Tunisia', ',', 'Rwanda', 'or', 'Kosovo', 'for', 'beef', 'and', 'dairy', 'use', '.', 'Mistreatment', 'has', 'been', 'well', 'documented', '.', 'In', 'a', '2018', 'report', 'on', 'farm', 'animal', 'welfare', 'the', 'European', 'Court', 'of', 'Auditors', 'noted', 'Ireland', 'was', 'guilty', 'of', 'infringing', 'calf', 'transport', 'rules', '.', 'A', '2016', 'investigation', 'by', 'Compassion', 'in', 'World', 'Farming', 'showed', 'that', 'some', 'journeys', 'were', 'lasting', 'over', '27', 'hours', ',', 'in', 'cramped', 'conditions', 'with', 'no', 'drinking', 'facilities', '.', 'In', 'May', '2019footage', 'taken', 'at', 'a', 'at', 'a', 'French', 'control', 'post', 'appeared', 'in', 'Cherbourg', 'by', 'animal', 'activist', 'group', 'Eyes', 'on', 'Animals', 'appeared', 'to', 'show', 'calf', 'mistreatment', '.', 'There', 'is', 'also', 'concern', 'about', 'slaughter', 'conditions', 'in', 'non-EU', 'countries', '.', 'But', 'the', 'Irish', 'government', 'has', 'repeatedly', 'pledged', 'its', 'support', 'for', 'the', 'export', 'sector', 'and', 'has', 'encouraged', 'companies', 'to', 'form', 'their', 'own', 'representative', 'organisation', '.', 'In', 'January', '2017', ',', 'Ireland', '’', 's', 'minister', 'for', 'the', 'Department', 'of', 'Agriculture', ',', 'Food', 'and', 'the', 'Marine', '(', 'DAFM', ')', ',', 'Michael', 'Creed', ',', 'reportedly', 'told', 'the', 'Irish', 'Farmers', '’', 'Association', 'annual', 'meeting', 'that', 'the', 'live', 'export', 'trade', 'was', '“', 'critically', 'important', '”', '.', 'There', 'has', 'been', 'a', 'rise', 'in', 'the', 'number', 'of', 'cattle', 'exported', 'over', 'recent', 'years', 'although', 'the', 'value', 'of', 'industry', 'for', 'livestock', '–', 'at', 'about', '€110m', '(', '£94m', ')', 'a', 'year', '–', 'has', 'declined', '6', '%', 'year-on-year', '.', '“', 'It', 'is', 'not', 'boosting', 'the', 'economy', ',', '”', 'Caroline', 'Rowley', ',', 'from', 'Compassion', 'in', 'World', 'Farming', ',', 'said', '.', '“', 'It', 'is', 'not', 'stimulating', 'prices', ',', 'live', 'export', 'volumes', 'are', 'going', 'up', 'each', 'year', 'but', 'factory', 'prices', 'are', 'not', 'following', '.', 'It', 'is', 'not', 'helping', 'the', 'average', 'livestock', 'farmer', '.', 'The', 'only', 'winners', 'in', 'this', 'outdated', 'industry', 'are', 'the', 'exporters.', '”', 'Colorado-based', 'animal', 'science', 'expert', 'Dr', 'Mary', 'Temple', 'Grandin', 'told', 'the', 'Guardian', 'that', 'from', 'a', 'welfare', 'perspective', 'slaughtering', 'very', 'young', 'calves', 'is', 'likely', 'more', 'humane', 'than', 'a', 'long', 'journey', 'by', 'sea', 'without', 'milk', 'replacer', '.', 'The', 'drawback', ',', 'she', 'said', ',', 'was', 'a', 'potential', 'food', 'waste', 'issue', '.', 'Asked', 'what', 'happens', 'to', 'their', 'slaughtered', 'calves', ',', 'New', 'Zealand', '’', 's', 'Ministry', 'for', 'Primary', 'Industries', 'said', 'in', 'an', 'email', 'they', 'are', '“', 'typically', 'processed', 'for', 'pet', 'food', '”', ',', 'which', 'goes', 'some', 'way', 'towards', 'addressing', 'Grandin', '’', 's', 'waste', 'concerns', '.', 'Although', 'no', 'farmer', 'would', 'endorse', 'the', 'idea', 'of', 'an', 'emergency', 'calf', 'cull', 'on', 'the', 'record', ',', 'there', 'was', 'agreement', 'on', 'individual', 'calf', 'neglect', 'issues', '.', 'Denis', 'Drennan', 'is', 'a', 'dairy', 'farmer', 'and', 'committee', 'chairman', 'with', 'the', 'Irish', 'Creamery', 'Milk', 'Supplier', 'Association', '.', 'Drennan', ',', 'who', 'has', '60', 'dairy', 'cows', ',', 'summed', 'up', 'the', 'reaction', 'of', 'many', 'farmers', 'to', 'the', 'idea', 'of', 'a', 'cull', ',', 'saying', 'if', 'he', 'was', 'shooting', 'animals', 'at', 'birth', ',', 'he', 'was', 'not', 'a', 'farmer', '.', 'Asked', 'if', 'male', 'calves', 'are', 'sometimes', 'left', 'to', 'die', ',', 'or', 'whether', 'there', 'are', 'farmers', 'who', 'only', 'call', 'a', 'vet', 'for', 'a', 'female', 'calf', ',', 'Drennan', 'said', 'yes', '.', 'Where', 'a', 'vet', 'might', 'cost', 'about', '€150', 'at', 'night', ',', 'domestic', 'prices', 'for', 'calves', ',', 'which', 'can', 'earn', 'farmers', 'anything', 'from', '€30', 'to', '€80', 'per', 'head', 'at', 'auction', ',', 'reportedly', 'fell', 'as', 'low', 'as', '€0.50', 'per', 'head', 'last', 'year', '.', 'Live', 'exporters', ',', 'meanwhile', ',', 'estimate', 'their', '2020', 'prices', 'would', 'stay', 'in', 'the', '€30', 'to', '€60', 'range', '.', 'Replying', 'to', 'questions', 'about', 'a', 'potential', 'emergency', 'calf', 'cull', 'Ireland', '’', 's', 'DAFM', 'said', 'by', 'email', 'that', 'it', '“', 'continues', 'to', 'engage', 'with', 'and', 'work', 'closely', 'with', 'stakeholders', 'in', 'relation', 'to', 'the', 'incremental', 'increase', 'in', 'the', 'number', 'of', 'calves', 'born', 'on', 'the', 'dairy', 'farms', 'over', 'the', 'past', 'few', 'years', ',', 'with', 'a', 'focus', 'on', 'rearing', ',', 'management', 'and', 'market', 'opportunities', 'for', 'such', 'calves', ',', 'including', 'opportunities', 'for', 'intra-community', 'trade', 'to', 'other', 'EU', 'member', 'states.', '”', 'The', 'department', '’', 's', 'statement', 'went', 'on', 'to', 'say', 'that', '“', 'in', 'addition', 'to', 'its', 'direct', 'oversight', 'of', 'animals', 'presented', 'for', 'export', '”', ',', 'it', 'will', '“', 'continue', 'its', 'range', 'of', 'inspections', 'and', 'controls', 'on', 'farms', 'and', 'at', 'sales', 'to', 'monitor', 'compliance', 'with', 'animal', 'welfare', 'standards', '”', '.', 'One', 'result', 'of', 'that', 'engagement', 'appears', 'to', 'be', 'a', 'December', 'DAFM', 'announcement', 'that', 'Irish', 'government', 'veterinary', 'inspectors', '“', 'will', 'accompany', 'calves', 'from', 'Ireland', 'to', 'the', 'control', 'posts', 'in', 'Cherbourg', '”', ',', 'without', 'prior', 'warning', '.', 'It', 'said', 'too', 'that', 'rubber', 'teat', 'water', 'drinkers', 'will', 'be', 'mandatory', 'for', 'unweaned', 'calves', 'from', '1', 'December', '2020', '.', 'Reacting', 'to', 'the', 'new', 'rules', ',', 'Eyes', 'on', 'Animals', '’', 'Nicola', 'Glen', 'said', 'she', 'was', 'pleased', 'to', 'see', 'the', '“', 'serious', 'welfare', 'issues', 'associated', 'with', 'the', 'transport', 'of', 'Irish', 'calves', '”', 'were', 'finally', 'receiving', 'proper', 'attention', '.', 'However', ',', 'she', 'said', 'it', 'was', 'unclear', 'how', 'the', 'vet', 'checks', 'would', 'work', 'in', 'practice', 'and', 'that', 'although', 'the', 'new', 'rubber', 'teat', 'rule', 'was', 'an', 'improvement', 'on', 'the', 'existing', 'system', 'of', 'hard-to-access', 'metal', 'pipes', ',', 'December', '2020', 'was', 'too', 'far', 'away', '.', 'Eyes', 'on', 'Animals', 'will', 'continue', 'its', 'inspections', 'until', 'it', 'sees', 'concrete', 'results', ',', 'she', 'added', '.']
<FreqDist with 686 samples and 1870 outcomes>
[(',', 88), ('the', 79)]
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Positive: 0.3145228215767635 Negative: 0.6406639004149378